Objective—: Tremendous efforts have been made to establish effective therapeutic neovascularization using adipose tissue-derived stromal vascular fraction (SVF), but the efficiency is low, and underlying mechanisms and their interaction with the host in a new microenvironment are poorly understood. Methods and Results—: Here we demonstrate that direct implantation of SVF derived from donor adipose tissue can create a profound vascular network through the disassembly and reassembly of blood endothelial cells at the site of implantation. This neovasculature successfully established connection with recipient blood vessels to form a functionally perfused circuit. Addition of vascular growth factors to the SVF implant improved the efficiency of functional neovasculature formation. In contrast, spheroid culture of SVF before implantation reduced the capacity of vasculature formation, possibly because of cellular alteration. Implanting SVF into the mouse ischemic hindlimb induced the robust formation of a local neovascular network and salvaged the limb. Moreover, the coimplantation of SVF prevented fat absorption in the subcutaneous adipose tissue graft model. Conclusion—: Freshly isolated SVF can effectively induce new vessel formation through the dynamic reassembly of blood endothelial cells and could be applied to achieve therapeutic neovascularization for relieving ischemia and preventing fat absorption in an autologous manner.

Cho SM, et al. J Lipid Atheroscler. 2021 May;10(2):202-209. https://doi.org/10.12997/jla.2021.10.2.202

With advances in deep learning and natural language processing (NLP), the analysis of medical texts is becoming increasingly important. Nonetheless, despite the importance of processing medical texts, no research on Korean medical-specific language models has been conducted. The Korean medical text is highly difficult to analyze because of the agglutinative characteristics of the language, as well as the complex terminologies in the medical domain. To solve this problem, we collected a Korean medical corpus and used it to train the language models. In this paper, we present a Korean medical language model based on deep learning NLP. The model was trained using the pre-training framework of BERT for the medical context based on a state-of-the-art Korean language model. The pre-trained model showed increased accuracies of 0.147 and 0.148 for the masked language model with next sentence prediction. In the intrinsic evaluation, the next sentence prediction accuracy improved by 0.258, which is a remarkable enhancement. In addition, the extrinsic evaluation of Korean medical semantic textual similarity data showed a 0.046 increase in the Pearson correlation, and the evaluation for the Korean medical named entity recognition showed a 0.053 increase in the F1-score.

The human body contains different endothelial cell types and differences in their angiogenic potential are poorly understood. We compared the functional angiogenic ability of human aortic endothelial cells (HAECs) and human umbilical vein endothelial cells (HUVECs) using a three-dimensional (3D) microfluidic cell culture system. HAECs and HUVECs exhibited similar cellular characteristics in a 2D culture system; however, in the 3D microfluidic angiogenesis system, HAECs exhibited stronger angiogenic potential than HUVECs. Interestingly, the expression level of fibroblast growth factor (FGF)2 and FGF5 under vascular endothelial growth factor (VEGF)-A stimulation was significantly higher in HAECs than in HUVECs. Moreover, small interfering RNA-mediated knockdown of FGF2 and FGF5 more significantly attenuated vascular sprouting induced from HAECs than HUVECs. Our results suggest that HAECs have greater angiogenic potential through FGF2 and FGF5 upregulation and could be a compatible endothelial cell type to achieve robust angiogenesis.

Owing to the prevalence of the coronavirus disease (COVID-19), coping with clinical issues at the individual level has become important to the healthcare system. Accordingly, precise initiation of treatment after a hospital visit is required for expedited processes and effective diagnoses of outpatients. To achieve this, artificial intelligence in medical natural language processing (NLP), such as a healthcare chatbot or a clinical decision support system, can be suitable tools for an advanced clinical system. Furthermore, support for decisions on the medical specialty from the initial visit can be helpful. In this study, we propose a medical specialty prediction model from patient-side medical question text based on pre-trained bidirectional encoder representations from transformers (BERT). The dataset comprised pairs of medical question texts and labeled specialties scraped from a website for the medical question-and-answer service. The model was fine-tuned for predicting the required medical specialty labels among 27 labels from medical question texts. To demonstrate the feasibility, we conducted experiments on a real-world dataset and elaborately evaluated the predictive performance compared with four deep learning NLP models through cross-validation and test set evaluation. The proposed model showed improved performance compared with competitive models in terms of overall specialties. In addition, we demonstrate the usefulness of the proposed model by performing case studies for visualization applications. The proposed model can benefit hospital patient management and reasonable recommendations for specialties for patients.

Angiopoietin-1 (Ang1) plays a crucial role in vascular and hematopoietic development, mainly through its cognate receptor Tie2. However, little is known about the precise role of Ang1 in embryonic stem cell (ESC) differentiation. In the present study, we used COMP-Ang1 (a soluble and potent variant of Ang1) to explore the effect of Ang1 on endothelial and hematopoietic differentiation of mouse ESCs in an OP9 coculture system and found that Ang1 promoted endothelial cell (EC) differentiation from Flk-1(+) mesodermal precursors. This effect mainly occurred through Tie2 signaling and was altered in the presence of soluble Tie2-Fc. We accounted for this Ang1-induced expansion of ECs as enhanced proliferation and survival. Ang1 also had an effect on CD41(+) cells, transient precursors that can differentiate into both endothelial and hematopoietic lineages. Intriguingly, Ang1 induced the preferential differentiation of CD41(+) cells toward ECs instead of hematopoietic cells. This EC expansion promoted by Ang1 was also recapitulated in induced pluripotent stem cells (iPSCs) and human ESCs. We successfully achieved in vivo neovascularization in mice by transplantation of ECs obtained from Ang1-stimulated ESCs. We conclude that Ang1/Tie2 signaling has a pivotal role in ESC-EC differentiation and that this effect can be exploited to expand EC populations.